As is known in the art, the meniscus is a pad of cartilaginous tissue that separates the femur from the tibia. It serves to disperse friction between these two bones. The cruciate ligaments (anterior: ACL and posterior: PCL) connect the femur to the tibia and are the most critical ligaments to image within the knee, as they are commonly torn during sports related dislocation, torsion, or hyper-extension of the knee.
When imaging the meniscus and cruciate ligaments with MR scans, the usual plane selected is a transverse (axial) view. However, a more appropriate plane for imaging these anatomies is that which is adjacent to and also connects the lateral and medial condyles of the femur. A clinical technician could manually define this plane by following a specific procedure to identify the following landmarks as shown in FIG. 1: a) the center of the pit between the medial and lateral condyles (fossa intercondylaris); b) the posterior margins of the medial and lateral condyles of the femur in the same axial slice (axial intercondyle line); c) the lower margins of the medial and lateral condyles of the femur in the coronal view (coronal intercondyle line). The realignment of the localizer along the plane defined by these new directions yields an acquisition in the knee frame of reference which is much more appropriate for diagnosis. It is desirable to have automated and reproducible precision planning by automatically determining this plane for precise scans. One technique for automated scan planning for knee joints was described in a paper presented by D. Bystrov, V. Pekar, S. Young, S. P. M. Dries, H. S. Heese, and A. M. van Muiswinkel, “Automated planning of MRI scans of knee joints,” in SPIE Conference Series. March 2007, vol. 6509 and its clinical accuracy was evaluated in F. E. Lecouvet, J. Claus, P. Schmitz, V. Denolin, C. Bos, and B. C. Vande Berg, “Clinical evaluation of automated scan prescription of knee MR images,” Journal of Mag. Res. Imag., vol. 29, pp. 141-145, 2009. The method of D. Bystrov et al above, uses an optimization scheme that uses an active shape model to guide a deformable model, which is in turn attracted to locally detected surfaces. The method handles bending of the knee with an explicit bending parameter that is also estimated.